Trade-off between unavailability and uncertainty in nuclear industry: An application of multi-objective genetic algorithm approach

The assessment and reduction of risk, utilizing the probabilistic safety assessment methodology, are the main prerequisites for improvement of safety in nuclear power plants. This need is even more emphasized nowadays due to the impact of ageing, since the number of safety systems components, that are approaching their wear-out stage, is rising fast. This study addresses the trade-off between risk and uncertainty in terms of deriving optimal test and maintenance schedules. The paper presents an approach for multi-objective optimization of surveillance requirements and its application on selected standby safety system in a pressurized water reactor nuclear power plant. The multi-objective optimization algorithm, utilized herein, is based on genetic algorithm technique. Components ageing data uncertainty propagation on system level is assessed by utilization of Monte Carlo simulation technique. The obtained optimal surveillance test intervals show that the risk-informed surveillance requirements differ from existing ones in technical specifications, which are deterministically based.